app.py

import streamlit as st
import pandas as pd
import google.generativeai as genai  # Import Generative AI library
import os
from pymongo import MongoClient
from db import insert_data_if_empty, get_mongo_client  # Import functions from db.py
from transformers import pipeline  # Import Hugging Face transformers for sentiment analysis

# 🔑 Fetch API key from Hugging Face Secrets
GEMINI_API_KEY = os.getenv("gemini_api")

if GEMINI_API_KEY:
    genai.configure(api_key=GEMINI_API_KEY)
else:
    st.error("⚠️ Google API key is missing! Set it in Hugging Face Secrets.")

#### **1. Ensure Data is Inserted Before Display**
insert_data_if_empty()

#### **2. MongoDB Connection**
collection = get_mongo_client()

#### **3. Streamlit App to Display Data**
st.title("📊 MongoDB Data Viewer with AI Sentiment Chatbot")

# Show first 5 rows from MongoDB
st.subheader("First 5 Rows from Database")
data = list(collection.find({}, {"_id": 0}).limit(5))

if data:
    st.write(pd.DataFrame(data))
else:
    st.warning("⚠️ No data found. Try refreshing the app.")

# Button to show full MongoDB data
if st.button("Show Complete Data"):
    all_data = list(collection.find({}, {"_id": 0}))
    st.write(pd.DataFrame(all_data))

#### **4. Load Sentiment Analysis Model (RoBERTa)**
sentiment_pipeline = pipeline("sentiment-analysis", model="cardiffnlp/twitter-roberta-base-sentiment")

# Function to analyze sentiment
def analyze_sentiment(text):
    sentiment_result = sentiment_pipeline(text)[0]
    label = sentiment_result['label']  # Extract sentiment label (POSITIVE, NEGATIVE, NEUTRAL)
    score = sentiment_result['score']  # Extract confidence score

    # Convert labels to a readable format
    sentiment_mapping = {
        "LABEL_0": "Negative",
        "LABEL_1": "Neutral",
        "LABEL_2": "Positive"
    }
    return sentiment_mapping.get(label, "Unknown"), score

#### **5. AI Chatbot with Sentiment Analysis**
st.subheader("🤖 AI Chatbot with Sentiment Analysis")

# User input for chatbot
user_prompt = st.text_area("Ask AI something or paste text for sentiment analysis:")

if st.button("Analyze Sentiment & Get AI Response"):
    if user_prompt:
        try:
            # AI Response from Gemini
            model = genai.GenerativeModel("gemini-1.5-pro")
            ai_response = model.generate_content(user_prompt)

            # Sentiment Analysis
            sentiment_label, confidence = analyze_sentiment(user_prompt)

            # Display AI Response & Sentiment Analysis
            st.write("### AI Response:")
            st.write(ai_response.text)

            st.write("### Sentiment Analysis:")
            st.write(f"**Sentiment:** {sentiment_label} ({confidence:.2f} confidence)")

        except Exception as e:
            st.error(f"❌ Error: {e}")
    else:
        st.warning("⚠️ Please enter a question or text for sentiment analysis.")